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August 12, 2014

Instruction

Not a small portion of my students complain bitterly when the fill out their student evaluations of the courses I teach. The typical complaint goes like this:

He isn't organized. He frequently digresses into things that are not part of the subject and never tells us exactly what we are supposed to be learning.

A more eye-opening version goes like this:

He would not tell us what things were going to be on the test so we could focus on learning them. I felt totally unprepared for the exams.

Sadly such comments are becoming more frequent in the last ten years.

One of my pet peeves in education is the use of the term ‘instructor’ as a catch all for the person who is supposed to be teaching. Even dictionary.com blurs the distinction, essentially equating the two terms. And in vernacular usage people do not make much of a distinction. But if you consider the root of instructor, instruct, you get closer to the issue. To instruct means to give orders (British dictionary version) for how to do something. That is, an instructor gives instructions in the performance of a task. The term applies to, for example, flight instructors or combat instructors who give specific directions in algorithmic like sequences to people who have to learn a procedure. Such learning has little to do with the growth and development of the intellect. It is not intended to enhance thinking. Quite the opposite; it is intended to instill the procedure in tacit knowledge so that the performer doesn't have to think when doing.

Contrast that to the idea that a human being should learn how to acquire concepts, to modify them in light of new information, to manipulate them in the mind to look at them from different perspectives, and, most important, to try combinations of concepts to see if something new, a meta-concept, emerges. In other words people need to be able to think. I had always believed that the objective of ‘higher’ education was to teach people to think. And not just higher education; I believed that the whole education enterprise should be dedicated to teach people both how to think and how to do things automatically that would support their success in thinking. For example, when learning mathematics you do need to be instructed in procedures but you also need to understand what those procedures are for in real life.

Teaching should encompass both instruction in procedures, a process to guiding students to the information they will need, and challenging them to engage in thinking about concepts they construct in their minds. All of these processes are needed in order to teach students to become fully functional thinkers.

What my complaining students were telling me is that they only wanted me to instruct them in the sense of how to prepare to take an examination. They were frustrated because their entire previous experience in education appeared to be this instructional process. People generally feel comfortable receiving instructions, following them, and then being assessed on their success in assimilating the instructions. And if their entire experience had been along these lines it would be understandable that not being instructed, while being expected to construct knowledge and understanding would be extremely stressful. But that is OK. We all need to learn how to deal with stress and we especially need to learn how to learn. Your boss will not be your instructor. That isn't her job.

This last spring quarter I had some frank conversations with a few freshmen students who were in my core course on systems science. I asked them how they learned in high school, what did they have to do to succeed in their classes. I was prompted to do this because a large number of them claimed to have taken various science courses such as biology or chemistry (and claimed to have liked the subject), yet they could not answer very simple questions I would ask about those topics (I use a Socratic method style in lectures). They would say things like, “I remember we talked about that, but I don't remember exactly what it was.” How could someone take a course in biology in this day and age and not have learned something like the mechanisms at work in genetic transcription and translation for protein construction? This is so fundamental to understanding biology it seems incomprehensible that the schools are not teaching it. Yet the students proclaimed that it wasn't something that had been emphasized. I have one of my son's biology text from high school at home so I checked it out on the subject. Sure enough it is covered quite well. Any student who had actually studied this should have come away with a fairly good grasp of how the cell manufactures all its component parts. The same is true for metabolism. The book covered it well, but the students could barely remember what it was, what it did, or why.

So on probing deeper what they told me was basically this. In school they are told what is going to be on the test, explicitly. That is they are told the nature of the questions they will be asked so that they can memorize little factoids from the text books. This is almost ritual and it applies most strongly to subjects that are related to the standardized high-stakes testing associated with No Child Left Behind or Race to the Top. However the practice appears to be wide-spread through many academic subjects. I could see why my students were complaining now.

I would not tell them what was going to be on the test for several reasons. First I consider all of the subject to be relevant or I wouldn't have included it in the curriculum. But also, the tests I give are not the so-called (and terribly misnamed) objective exams. The latter is fine for testing the assimilation of specific facts and procedures. You can test someone's success at memorizing, for example, a mathematical procedure, like solving a quadratic equation, with such tests. There are right and wrong answers. But those kinds of tests do not assess how well the student can think. For lower-division courses I give exams that are a mix of fact/procedure learning and thinking using those facts and procedures. These students are new to the subjects and they need to build a lexicon of terms, simple relations, and procedures for things like solving problems and communications. But they also need to start getting used to displaying an ability to think about the subject. So at least 50% of the exams will be asking open-ended questions (short answer) for which there are good answers (i.e., putting ideas together in a logical way) but there are not purely right answers. Many, perhaps most of the students hate this.

In upper division courses, especially electives, the situation changes and all of my exams and project assignments require a high degree of thinking. Generally most of the students have finally matured to a point that they can understand the reasons for this, though there are still a few who complain.

There are probably several factors that contribute to the devolution of education to becoming essentially vocational-like instruction. Certainly the NCLB-type high stakes testing that has become the norm for so-called ‘accountability’ has reinforced the practice since the emphasis on passing the tests puts pressure on teachers as well as students. Their jobs are on the line. The whole system is pressed to performance as defined by objective measures, a significant proportion of students producing ‘right’ answers. But the tendency to rely on objective exams has been a feature of school education for a long time. Let's face it. Such exams are easy to prepare and grade. They only probe the surface of student learning and reduce the burden on teachers, especially considering the issue of class sizes. Since the measure of student success is the grade point average (GPA) and teachers surely want to see their students succeed, it is quite natural that they resort to teaching to the test. The pattern is widespread and deeply established in the hall of academia. Additionally the use of student evaluations of courses, by administrators desperate for a convenient “objective” measure of teaching effectiveness, again for accountability, produces a subtle pressure on teachers to ‘please’ the students. Their actions need not be the result of conscious decisions to do so. The effect comes through the subconscious bias that follows from basically understanding that their jobs could be on the line if the students aren't happy. In public higher education this effect is amplified by the issue of “retention”, or keeping students in school so that the graduation numbers look good. If you flunk out too many students or too many quit from frustration then you look bad and the social milieu will frown on you.

Irony of Ironies

Hardly a week goes by when I don't read a news item about how American education is failing to produce enough scientists and engineers. Many articles are about how schools are failing. The calls for reform are echoed from every corner of society. I talk directly to employers who are very vocal about how graduates they hire can't think. They can't communicate. They can't learn on their own. They might have good skills (say at programming), but cannot figure out how to analyse complex problems or design complex solutions.

The louder the din is about how education is failing the more pressure there is to double down on accountability. And since the only thing most educators know how to do is focus on facts and procedures, that is where the effort will go. It will accomplish exactly the opposite of what is needed.

More ironic still is the belief that somehow on-line education will solve these problems. I expect it to exacerbate the situation considerably. My own opinion is that on-line learning might work for, say, special topic courses, especially graduate level courses in professional fields. But to believe that whole baccalaureate programs can be delivered on-line and that the students will actually learn how to think in that subject is to be ignoring everything modern psychology of learning has to tell us about how people actually learn.

The more our society panics about the failures of education the more they will do the exact wrong things. They want an inexpensive, quick solution to education reform. But the reality is that there is no magic bullet that can replace teachers who help students learn to think. It is inherently an expensive enterprise. Society invests in doing a good job so that it will benefit in the future when those graduates are active in the workforce and as citizens. But society has chosen to do the exact opposite. They want to find cheap ways which invariably rely on putting the burden (as well as the blame) on teachers. And that attitude will simply produce a much worse situation.

I love my job. I love working with students who have finally gotten the fact that learning isn't the job of the teacher. I love being able to explore ideas such as I write about in these blogs. I love to exercise my intellect and challenge students to do the same. But there are parts of the job that are definitely unlovable! I do not like the whole process of assessment based on there being right and wrong answers. I do not like the grading system that only rewards successes and punishes failures when, in fact, most people learn more from failures than successes. I do not like having to be the bad guy who forces lower-division students to have to think when they have literally been trained to not do so. But I accept these negative parts and the responsibility for being accountable to their long-term interests even as they see me as their enemy. It goes with the job.

Every once in awhile I get a communique from a former student who has been out in the world for a while. And those communiques make it all worthwhile. A not atypical message will say something to the effect:

I have to confess when I took your course I really hated it. But I have found out that you were really right about learning to think. I started practising what you said and today I am a chief designer for my company. Just wanted to say thanks. And also apologize for giving you low scores on the student evals.

Teaching isn't (just) training. It is a whole constellation of guidance skills. But mostly it has to be about getting students to think well. It has become a steep uphill battle in recent years thanks to society's penchant for cheap and fast preparation for specialist jobs being the basis for education. I'm just glad I got to work in the field while there was still some semblance of rationality abiding. I just feel sorry for the masses who are moving inexorably toward deep ignorance as a result of being educated in the US.

August 08, 2014

An Example from Biology

A living system is the basic example of an economy. For example within a single cell the metabolic machinery is a production factory to produce more biomass, either growth of the cell to a mature size, or through cell division production of new individuals. There is a third product that some cells, those in multicellular organisms, make and that is simply replacement of degraded biomass, what we call a steady-state economy.

Figure 1 shows a simplified and abstract representation of cell metabolism, at least as far as things like protein synthesis and uses are concerned. The major work processes are the construction of enzymes (and enzymatic systems) and the machinery construction. Various processes, such as those shown, are actually complex molecular machines that require enzymes and energy to do their work. For example the ribosomes, the major protein synthesizer machine has to be constructed out of RNA molecules and proteins. All of the cellular organelle are constructed or self-organize based on their inherent chemistries.

The cell is basically an “exchange” economy where various machines produce products needed by other machines. Most often the trades go through several steps. In the background all of the machines tend to degrade and break down. Their molecules have to be either recycled or expelled. Many cells actually produce molecular products that are used by other cells, so the exchange model extends to whole multicellular systems as well.

Figure 1. Metabolism in a living cell is an example of an economic system.

Every cell captures materials and energy from its environment. Photosynthesizing cells (plants) have an energy capture and conversion machine, the chloroplast, that manufactures energy packets, like adenosine tri-phosphate (ATP) from sunlight input. Animal and fungal cells take in molecules that are both energy carriers and raw materials. They first need to digest organic materials (from plants) and then machines called mitochondria convert the carbohydrates into ATP. ATP is like a floating battery that circulates everywhere within the cell conveying convenient energy to all of the other machines. Every machine in the cell (organelles) use the energy stored in ATP to do their work. All of them employ proteins and membranes. The former can be either structural components or functional (enzymes). The latter, bi-layered lipoproteins, encapsulate the functions of the machines. Those boundary conditions regulate the flows of molecules so that rates of work are controlled.

Every machine, or metabolic processor, operates under feedback control. Certain molecules “sense” the products and provide either direct feedback to the machine (e.g. down modulating the production of certain proteins at the ribosomes) or escort the products off to the recycling center if they are not needed. Some escorts test the quality of the output, for example determine when a protein has not folded into its tertiary structure properly, and acting as quality control monitors send the deformed product off for digestion and recycling.

Living economies operate under fairly rigid rules (principles) with respect to the conservation of material and energy. For example the rule of “if you don't use it, you lose it” is followed rigidly. Cells don't keep useless organelles or molecules around if they are not actively contributing to the whole system. Such “excesses” are degraded and digested, recycled or expelled. And their production machinery will be down-modulated. No more energy will be expended on producing what isn't needed.

Another rule is: “If there is a greater need to produce product X, then up-modulate the machinery to make it.” When cells are placed under conditions that are unfavorable, where some critical factor like pH is near or beyond the limits of tolerance the cell responds by up regulating machinery that produces mechanisms to thwart or compensate it.

These rules reflect the laws of supply and demand.

The depiction in figure 1 represents the kind of organization and processes that take place in the cytoplasm outside of the nucleus. The basic controls regulating metabolism are operational controls (as covered in the last post). Everything is tightly coupled through feedback loops and under “nominal” conditions the market of exchanges and valuation based on energy consumption works cooperatively without a great deal of intervention. But nominal conditions hardly prevail for long. Every cell sits in an environment where fluctuation in critical factors constantly impose stresses that must be responded to, as described above. One of life's earliest accomplishments was the invention of homeostasis. This is the basic feedback control loop for maintaining critical factors within a nominal range by reacting to external changes. A homeostatic mechanism can either do something actively to influence the external conditions, or respond by activating a movement of the organism away from the situation (where movement is possible) or call upon internal reserve mechanisms to counter the external influence, for example by sequestering molecules when their concentrations are driven too high. Homeostasis is the first level of tactical management, coordination with the external world in order to maintain function. It also demonstrates the tight link between operational control and tactical control. Figure 2 depicts the components of a homeostatic system.

Figure 2. Homeostasis.

The basic physiological process depicted could be any of the producing machines described above. It needs and input of some critical factor to do its job. But the critical factor is influenced by something in the environment that can drive that factor above or below its necessary level. The control system monitors the input generating a feed forward error signal, eff, which is used by the sub-process I've called the ‘control model’ to generate a control signal, cr that activates the response mechanism. That mechanism is capable of doing something that counteracts the environmental influence. This diagram shows an arrow from the response to the environmental factor, but the mechanism might act internally, for example to actively sequester or neutralize unwanted molecules.

The control model might be a complex one. I've included the basic feedback control loop for the physiological process product output. The comparator generates an error signal, efb, that can be used by the model to send a control signal, cp to the process to take internal corrective action. The model, thus, represents a close cooperation between an operational control (feedback) and a simple tactical control (feed forward).

Also shown in the figure is a depiction of a higher-order mechanism that embodies the two principles mentioned above. The response mechanism needs to be maintained at some level of readiness that is just necessary for the ordinary sorts of responses it has to make. The ‘response constructor’ is responsible for this maintenance. Imagine its “responsiveness” as represented by the size of the oval. It can't be too big because its maintenance would be prohibitive in terms of energy and material required. At the same time it cannot be too small because if the external influence were to drive the critical factor out of range too fast it would result in some kind of damage to the organism that could be fatal. How to determine what the right size would be?

The response constructor uses some kind of molecular memory device that keeps, essentially, a time averaged trace of the history of external influences (supplied by the control model). If the memory value is high then it means the influence has been strong and frequent over some past time window. Thus the response constructor “knows” to build up and keep strong the response mechanism. It will invest more material and energy into the response mechanism because it “expects” there will be an on-going need for fast and strong responses in the future.

The details of the constructor and its memory are not important right now. I will just say that this basic kind of machinery is active in all biological systems where some anticipation of future demands for a function is needed to prevent damage or take advantage of an opportunity. In a more elaborate and multi-time domain form, this is the basis for what neurons do when they encode memory traces in their synapses. Hence the term memory is not abused here.

You may recognize the above figure as it is basically the same as figure 6 in the prior post in this series. Here I have added the response constructor to expand the example as an exchange economic model.

In the metabolic economy there is a true ‘currency.’ That currency is the packets of energy called ATP. Energy is involved in all transactions and, by the second law of thermodynamics, degrades in capacity to drive work as it flows through the economy. It is given off as waste heat and new high grade energy must always be supplied.

The messages depicted in these figures are conveyed by chemical signaling. Small weight molecules are released by one sub-process and diffuse through the cytosol to be ‘received’ by another, target, sub-process that will then act on that message. Cells have receptor sites on the outsides of their membranes that are specialize to couple with these diffusing molecules. When that happens it generally sets off a cascade of so-called second messengers that eventually affect an internal control mechanism that then does its work (having access to ubiquitous ATP) and responds to the signal the cell received. That is what is actually going on in figure 2 with all of the signal arrows representing various molecules that activate the machinery, such as the response mechanism. Chemical signaling was the earliest form of communications in living systems, both internal and external to the cell membrane.

But the point is that the cellular metabolic economy is regulated by the same hierarchical control system covered in the last post. When we include the role of the genes, along with their network of expression controls and epigenetic mechanisms, we will find that it all fits the model shown in figure 8 in that post.

There is one more aspect to the biological model of economy governance that should be brought out. Cells do not grow bigger and bigger forever. There are constraints on how large a cell of a particular type can get to be. Some constraints may be imposed by external factors, others, like effectiveness of heat dissipation, may be imposed by internal factors. Newly created cells, however, are smaller than the optimal size and so they follow the mandate to convert materials and energy in to new biomass within their membrane. Once they reach the optimal size however they have the potential to replicate by cellular division, thus making two small cells that then, each, continue to grow biomass. They do this as long as there are no external constraints, such as lack of a vital material or energy, to cause them to stop growing and simply maintain.

In the case of multicellular organisms the same pattern can be seen but with an interesting twist. External factors, that is external to the multi-celled tissue that is growing, can trigger internal signaling within the tissue to stop the reproduction of more cells. This is what happens, for example, in the development of organ tissues in embryos and fetuses. Cells receive signals that not only tell them what to differentiate into, but also when to cease growth activity or at least modulate it to fit with the overall organism growth pattern. No one tissue can exceed its natural size. Figure 3 depicts this form of restraint on growth.

Figure 3. The biological mandate dictates that more biomass be produced until some forms of constraint trigger restraint.

Biological systems evolved these self-restraint in the face of external constraints in order to preserve life. Any overrun by any one biological entity threatens the life of all other organisms and therefore mechanisms for suspending the biological mandate were needed to achieve balance in the whole ecosystem. The regulation mechanisms are many in form but you will find them at all levels of living systems. And you can see the effects when they fail to work. This is what cancer is, a breakdown in the growth-regulating system releases the suspension on the mandate and the cells resume growth and reproductions indiscriminately. I'll return to this idea later.

The Biophysical Economy

The human economic system is effectively the same model as the cellular metabolic economy. The roles of materials and energy are the same. The work processes needed to construct products needed by other processes are basically the same. Even the purpose of the whole system is the biological mandate of growing more human biomass.

In figure 4 I have drawn yet another view of the biophysical economy, abstracting all of the basic functions into just a few representative sub-processes. Fundamentally the economy is designed to extract natural resources from the Ecos as well as capture sources of energy to then produce all of the goods and services that ultimately go to consumers through a distribution subsystem. If you trace through the flows and processes you will see that this schema is similar to what was shown in figure 1. Unlike the cell example the human economy has a much smaller recycling capability (so it is not shown).

Figure 4. The biophysical economy is shown in a very abstracted form.

Since I have written a considerable amount about the biophysical economy (and biophysical economics) I won't go into details here (look for more in the Biophysical Economics section of the blog). Rather I want to call attention to the signal arrows in this figure, the thin black and green arrows. These are the cooperation signals the provide supply and demand messages between processes. In the past I have claimed that money is really just a form of signal, information about the amount of usable (free) energy that can be controlled in the sense of directing which work is to be done (see figure 5 below). Unfortunately in a debt-based situation such as we have today money is a very distorted message conveyance. That is one reason that our current economic system, world wide, is not working very well. The governance model (essentially free markets with light regulations on selected processes) relies too heavily on cooperation and that depends on the fidelity of inter-process signals. As already argued, when any system gets too complex it is necessary to introduce coordination (logistical management) between processes in order to facilitate the functions in figure 4.

Figure 5. Money is used to convey information regarding the flows of goods and services. Individual agents decide how much money (the intensity of the message!) to send. The receiver interprets the message to determine how much work to do and thus how much energy to expend.

What this really means is that a workable governance model must be based on effective communications and realistic logistics rules. The governance we have was born from a very nebulous set of ideas about the interactions between government, political process, and the economy, hence the name political economy. The system is a result of an evolutionary process but with a kind of built-in bias toward the idea of progress. Unlike all previous forms of social evolution (e.g. emergence of eukaryotic cells from bacterial cells, emergence of multicellularity, etc.) the evolution of the political economy has been nudged along by the reflective agents who have tried to shape what it would be. It was as if certain genes in the genome ‘thought’ about what they wanted and mutated themselves accordingly. The whole system is impacted by ideology-based decisions, and generally not for the better. Overlay the complexification of society due to technological development and you have the evolution not of a sustainable system, but an aggregate of many dysfunctional processes.

Economic Governance

Consider the history of economic systems that have come into existence since the advent of agriculture. The original governance of agriculture-based societies was based on the need to reliably produce food stuffs for the society. Governance began with the specialization of those who could see the larger picture, not just how to plant seeds, but when and where to plant. The early Egyptians, for example, organized around the management of water from the Nile river and the land immediately nearby. A coordination function emerged quite early in the form of early kings (probably derived from “headmen” in nomadic tribes) in neighboring territories who took on the role by managing the administration of things like granaries and the emergent functions of ‘surveyors’ who specialized in measuring out the land areas after the annual Nile floods had receded. After the invention of the plow and the domestication of animals, along with the increasing capabilities to work with metals and clay, specialist trades developed rapidly. The production of products of these specialists needed to be organized and coordinated since any one specialist might be losing track of what the others were doing. Someone had to rise above the whole operation and help make sure what needed to be done was done.

The model of governance that emerged in Egypt and five other similar civilization centers was based on a hierarchy of command and control. The kings became Pharaohs, god-kings, who had absolute authority. They presumed their knowledge was absolute. As the complexity of the kingdom rose layers of administrators were rapidly added. The Pharaoh became more distant from the workers in the fields. A class system based on the tendency in human psychology to establish some kind of pecking order was amplified by the nature of the hierarchy. This would establish a pattern that would be with us for the rest of our experience. A human bureaucracy superposed over the natural management hierarchy carried all of the flaws of human psyche, especially the lack of adequate sapience to counteract the limbic system's tendency to drive the need for establishing power relations. It has never been a particularly happy combination.

Among the duties of the ruling class, by virtue of their nominal positions, was the protection of land holdings — the territory of the kingdom. A separate specialization developed early on, that of the warrior and the armed forces necessary to protect the kingdom from marauders. At first their jobs were probably mainly defensive but as time went on and kingdoms experienced bad harvest from time to time, the idea of invading another territory probably followed very naturally. Of course it might not have taken a hard time to promote the notion of aggression. Humans are already individually aggressive and greedy (again a lack of sapience thing). So the temptation to invade another kingdom for booty or outright takeover was probably not a hard hurdle to jump.

The basic form of hierarchical governance with class and power overlays has been with us ever since. Even the American and European experiments in some forms of democracy have not been able to rid us entirely of this structure. For example the American presidency, which George Washington explicitly demanded not be like a king, has evolved to a king-like status. We even have a modern form of dynasty in families like the Roosevelts, the Bushes, and the Clintons. The British, of course, never got rid of their royalty, going back to feudal days.

What is wrong with governance is the humans who implement it. A human being is a selfish, self-centered, limited-perspective agent who is placed in the untenable position of making cybernetic decisions with weak knowledge and distorted senses. No mere mortal man (or woman) can be the president of the United States, or leader of any state on the planet. The philosopher kings are rare these days.

Human Agents

Recall figure 4 in the prior post, reproduced here so you don't have to go back to that. We human beings are, ourselves hierarchical cybernetic systems. Our brains are designed to process operational level, coordination level and strategic level information. We make decisions at all of these levels. But, and this is a huge caveat, we are driven by the biological mandate and our sapience capacity is still very immature. In figure 6 I've added the limbic system that drives much of human decision making. In higher sapience the feedback from the strategic level of decision making acts to down modulate the emotional and purely biological drives that influence our thinking and reactions. In ordinary sapience (i.e. the average brain) this feedback is weak and the influences from the limbic system will ultimately dominate decision. This is the basic reason that economic agents are selfish and mostly irrational.

Figure 6. The irrational and selfish agent is motivated by limbic drives and desires.

Human beings are the worst agents for a hierarchical cybernetic system since the inject their own desires into the decision process. Moreover they are largely plagued by incomplete or even wrong knowledge about how the world works. Libertarians are the worst. They completely deny the need for a regulation system. But I have to admit that their instincts might be right given that it is human agents that are doing the regulation. A political economy needs a hierarchical cybernetic framework. What it does not need is human decision makers who are so lacking in sapience.

But suppose that the agents in a hierarchical cybernetic system could be more sapient. In effect it would mean an expanded strategic thinking ability along with expanded systems and higher-order judgment ability. Such an agent would not have lost their limbic drives; evolution doesn't work that way. Instead what the expansion of the prefrontal cortex associated with increased sapience would mean is a stronger ability to down-modulate the activities of the limbic system that might have driven poor, selfish decisions. Such individuals would have stronger cooperativity motivation and more empathetic capacity; they would be hyper-social creatures. In short, they would be wise. Figure 7 depicts such an agent. I call such an agent an “adaptive, evolvable agent” because their expanded abilities include being able to deal with uncertainty, ambiguity, and especially an ability to modify their own concepts (beliefs) in light of new evidence. Their thinking is capable of evolving with changing conditions.

Figure 7. An adaptive, evolvable agent is one with greater sapience than we currently see in most people. Greater sapience would include expanding on strategic thinking in each agent, but would also mean the agent would be hyper-social, quite ready and motivated to cooperate with fellow agents.

In my next post in this series I will attempt to construct a hierarchical cybernetic system for a human economy that would be long-term stable (dare I say sustainable?) Note that I said 'a' human economy. Given the situation with energy source depletion it cannot be the current economy. I'll base the design on the above concepts of a biophysical economy embedded within the Ecos framework and in balance with it. We can then ask, what governance might look like for such a system. We have to use my adaptive, evolvable agents as the decision makers in this structure, and I will offer the argument as to why this is so.

Granted that this is something like a Platonic ideal — we don't have a surfeit of highly sapient people to work with — but in order to understand where our real system is it might be useful to gauge against what the ideal might look like.

August 06, 2014

The bombing has actually helped shape my life. I was born on this day in 1945 and am always reminded of the fact. But more than that, the thought that I came into this world on the very day of the greatest terrorism act against civilians of all time (along with Nagasaki just a few days later) has been a driving force pushing me to find meaning in life. The current rhetoric, especially in the US, regarding the terrorism going on in the Middle East (particularly between Israel and Hamas) is just so empty. How was it possible for the US to bomb innocent civilians in one war and then call out anyone else who does the same thing is totally beyond my comprehension - except that I chalk it up to the meaning I have found, human beings, as a species, are really not sapient.

Please remember.

On another, happier note: my textbook, "Princples of Systems Science", is being typset as we speak! Still on track for autumn 2014!

July 31, 2014

The Fundamentals of Economics

I'd like to examine the political economy from the perspective of systems science. Specifically I want to put biophysical economics together with the theory of hierarchical cybernetic systems to see if we can't find some guidance to developing a model of a well regulated, functional, and maintainable economic system. Political economy is the study of the intersection between the system of governance and the economic system that supports those participating in the society. There are quite a few political theories regarding this intersection, from strict forms of socialism to anarchy (no governance) wherein the free markets will self-regulate. But these political theories are inevitably based on ideological frameworks. In the end they are supported not by evidence or even logic, but by emotions and “gut feelings.” Here I want to do an analysis of a biophysical economic system, i.e. one based on the scientific realities of energy, work, wealth, and the laws of thermodynamics, that asks what might we find as scientifically based models of regulation that, in particular, keep the system in balance with the Ecos and maintained over an indefinite period of time.

My analysis begins with the fundamental components of any economic system, the individuals that participate in a social contract involving specialization of work skills and trades of wealth. One might be tempted to think of this as the ultimate microeconomic framework.

To be clear, economics involves the control of behaviors that contribute to the obtaining of resources needed to maintain a biological system, including those needed to extend the system into the future, e.g. reproduction. The biological mandate to continue to exist and reproduce is paramount. Every human being is bound by this mandate and all economic decisions are subject to its dictates.

In spite of all the seeming complexity and sophistication of human behavior, ultimately it is all directed at fulfilling this mandate. We can more easily see this by looking at human evolution. What were we when we evolved to our current form? What were our behaviors as creatures of nature? The exposure of our basic biological heritage can be gleaned from this background.

Figure 1, below, depicts this unit of economic activity, an individual. I've chosen to start with an individual from a pre-agriculture time to show how this is basically a biological entity. Energy flows from the sun to plants and from there to animals and humans (some via animal protein). Humans can then do physical work using resources from nature to produce various tools, such as stone cutters and scrapers, wooden digging sticks, and clothing, that all give the human leverage in obtaining the natural resources.

Figure 1. Early Homo sapiens obtained life support through hunting and gathering getting energy from plants and animals. Human labor, in the form of fashioning useful tools allowed humans to more efficiently obtain the energy, which was a major evolutionary advantage.

By contrast consider the modern state of affairs. Each individual human is now amplified in their work capacity by the burning of fossil fuels for 80%+ of their high-power work needs. Figure 2 depicts this situation. Humans, by virtue of producing power tools can do considerably more work than they could by hand. So they produce more product (or service) per unit of time and wealth (the aggregate of products and services) accumulates. Some of those power tools increase the human effort efficiency (not necessarily the work efficiency), which is called “productivity”, a seemingly good thing. When humans were few in numbers, even after having mastered water and animal power to improve their productivity, there seemed to be enough energy and material resources so that the economic activity of individuals could expand giving rise to accumulation of wealth. Surely that was a good thing!

Figure 2. The use of external high-power fuels to drive automation amplifies the human capacity for work many fold.

The individual unit as depicted in figure 2 will be the one we will use later. Returning to the early human situation, consider figure 3 below. This is the fundamental unit of social economics. The basic idea was recognized by Adam Smith as the inherent capacity for certain individuals to become specialized in one kind of work wherein they are very efficient. The products so produced, however, have wider appeal and so two (or more) individuals producing excesses over their own needs may enter into a trade relation in which they negotiate an exchange based on some perceived relative value per unit of their products. That is they barter.

Figure 3. A simple model of specialization and trade. One human produces product A and needs or wants product B. The reverse is the case for the second individual. They negotiate a trade on the basis of some perceived unit value of each product.

Incorporated into figure 3 is the now more prominent material resources such as wood and fiber as well as rocks and, later, bronze metal. The situation represents, roughly, the transition period from the Paleolithic to the early Bronze Age. Humans were in the process of mastering building and shaping arts, becoming proficient in agriculture and animal husbandry, and engaged in trade on an inter-individual and inter-group basis.

The figure shows the flows of matter, energy, and influence (messages). Each individual human is responsible for applying control to the work process (e.g. farming, making tools, etc.)

The cybernetic element in this model is the individual, an adaptive agent capable of making hierarchical cybernetic decisions and learning to change economic behaviors based on experience in various situations.

Economic Agents

Every human individual must make a range of decisions that guide their economic behavior. These range from purely operational level decisions (plant the seeds in this plot of land), to logistic (save enough seed from this harvest to plant next year), to tactical (obtain some new seeds from the tribe over the hill) to strategic (find a way to expand my territory). The individual agent thus embodies the whole hierarchy of cybernetic management with respect to their own individual behaviors. Some individuals were particularly good at different levels in this hierarchy. Some (a few) particularly good at strategic and tactical thinking were able to organize the activities of many members of a social unit. We will examine this tendency later.

In figure 4 I have provided a simple representation of an adaptive agent that will be developed as the fundamental economic agent. Bear in mind that all that motivates this agent is the biological mandate. Ultimately all decisions are directed toward maintenance and reproduction. But because the agent is adaptive and also under the mandate to maintain life at the least cost, it will find creative ways to accomplish its mission with the least risk while conserving energy.

Figure 4. An economic agent (an individual) is modeled as a decision maker with an ability to consider operational, tactical, logistical, and strategic decisions.

The human brain is a hierarchical cybernetic system that evolved in response to the increasingly complex world it had to survive in.

Things Get Complicated

I will start with a refresher on hierarchical cybernetics as realized in the human brain (the agent) and with examples form meta-biological social systems, especially commercial enterprises. All humans do some kind of work and produce things that they and others can use in carrying out the biological mandate. Figure 5 shows a work process in which an operational-level controller maintains the quality of the product through a feedback loop. If the product being produced gets out of specs, the operator (agent) generates a control signal or action that brings the product quality back into alignment with the desired values. Those values are, of course, determined by what the “customer” wants.

Figure 5. Work processes produce a product that is desired by a customer. The quality and quantity of the product requires close monitoring by the operational level management of the human agent.

The next higher level in the cybernetic hierarchy is the coordination level. Figure 6 shows the agent performing both feedback and feed forward control. The coordination level serves two basic purposes. The first is to coordinate the behaviors of some number of internal sub-processes within the basic work process (shown later). The second is to coordinate the overall behavior of the work process with the external environment, in particular, the sources of inputs and the sinks for outputs. In this figure I show a simple version of a coordination level controller using the beginnings of a tactical (pre-tactical) model and feed forward information from the input flows to send commands to the work process in order to modulate the input flows appropriately, for example by using internal buffers to store excess in times of plenty and supply internal sub-processes in times of want.

Figure 6. Coordination with the external world begins by monitoring the input flows against their desired values and modulating them to be within the desired ranges.

Logistical control handles coordination between internal sub-processes when a work process is complex. It is broken up into sub-processes, each of which has its own operational control and an overseer logistical controller.

Figure 7. Coordination of internal sub-processes involves logistical controls. The controller receives messages from the operational level controllers regarding time-averaged behaviors of the processes. It uses a balancing or optimization model to determine the best mix of operational control settings so that all of the sub-processes operate in synchrony (harmony).

In many situations (many different kinds of systems) the logistical control is somewhat automatic, requiring little in the way of “conscious” decision making; this is the case for the human brain, for example. Most of the regulation of internal subsystems is under non-conscious control, e.g. heart rate and breathing when exercising. In most large organizations (e.g. corporations) the routine procedures that are followed (codified in procedures manuals) are similarly automatic and senior management does not concern itself with the details of middle management's work until the profit and cost center numbers come in, say, at the end of the month. Even then they do not worry about the details but rather just reward or punish performance.

When a complex system is operating in a complex environment the demands on tactical control are great. The system must advance from merely checking flows in and out, but must actually have more information about the sources and sinks themselves. From the pre-tactical coordination to a fully tactical form we need new kinds of message processing that collects data from the external world and provides this to a full tactical controller. Whereas pre-tactical involves relatively simple real-time sensing, the tactical controller works over much longer time scales relative to the operations. It includes anticipatory models of the environment that can be used to predict future behaviors that could lead to variations in the resource inflows or customer demands, etc. Figure 8 depicts this situation.

Figure 8. A full tactical controller (evolved from the workings of the pre-tactical version) is able to sense the behaviors of external entities of importance to the system and use models of those behaviors to anticipate the near future, thus being able to devise tactical moves to minimize negative impacts or maximize positive ones for the whole system. The tactical and logistical controllers still need to communicate with one another to provide complete coordination, within and without.

Once again, the human brain is a wonderful example of this capacity. Our external sensory system is constantly taking in data about the state of the world while we are not sleeping (or daydreaming). It processes this data into percepts based on our memory system's images, which, in turn, feed into the concept processing areas of the brain. We are continually processing lower-level concepts, e.g. there is a wall in front of me, which we have learned to handle without much conscious attention (turn to avoid the wall). Our interactions with our social world takes much more processing and not a small amount of conscious attention since people are generally always surprising us to some degree or another. We have to have models of them to help us react to or anticipate their behaviors. Many brain scientists think this is the basis for the evolution of our larger brains, that our social interactions are so much more complex than those of our nearest relatives (apes) that we need much more processing capacity to interact tactically with other people.

Organizations, too, invest considerably into tactical coordination. The purchasing department, the sales department (with marketing), the shipping department, etc. are all examples of complex operations that deal with the external environment, attempting to maximize benefits to the organization (get the best price). Marketing departments in particular are a lot like the social tactical facilities in the brain (and this is actually not just an analogy) representing some very conscious considerations with respect to getting customers to want the products and be willing to pay good money to get them.

Tactical decisions are about how to win the battle; they are relatively short-term in scope. But decisions about which wars to wage, that is strategic, longer-term, and involving the likely changing of behaviors to achieve a strategic opportunity or avoid a threat. For this a new level of cybernetic management is needed that can process long-term plans and direct modifications to the internal structure or tactical models such that behaviors of the system change to accommodate long-term changes in the environment. This involves extending the time horizon and scope of the world models associated with tactical control. The new level is strategic/planning.

Figure 9 depicts the larger scope of strategic control. The strategic decision maker is receiving information from the tactical (and logistical) controllers but also receives information from the larger world, those entities with which it might not have direct interactions (clouds), but whose behaviors might impact the sources and sinks with which the system does interact (e.g., influence arrows from clouds to sources). This is more than just tactical control; it evolves from tactical control when the larger environment is highly dynamic, complex (e.g., bi-directional arrows between clouds), and non-stationary giving rise to a need to understand how the future will shape up in order to avoid new dangers and exploit new opportunities. Not shown in the figure, the strategic controller has the ability to “pre-adapt” functions and processes within the system such that new behaviors can emerge to make the system more competent in the future state of the world. The strategic control is responsible for long-term planning based on its best estimate of what that future world will be like.

Figure 9. Strategic control obtains information not just from the tactical level (based on the behaviors of sources and sinks) but also from other factors in the environment that may impact those sources and sinks. It looks at general models of these factors to try and set plans for changes the system need to make internally to take advantage of opportunities and avoid threats in the long-run.

Certain areas of the prefrontal cortex in the human brain perform this function. Most human beings do have a limited capacity to think about the future and lay plans. The brain certainly has the capacity to learn about the behaviors of other entities in the world that do not have a direct influence on the individual, but are part of a larger stochastic dynamic that could come to have an impact. The brain does have the ability to do some limited strategic planning and control, but whether the majority of people fully use this facility is an open question.

Organizations, similarly, have a strategic thinking function and tend to try to use it explicitly. However, because these functions are carried out by people, the true efficacy of such functions is subject to how well the individuals think strategically.

One key difference between strategic and tactical control involves the capacity of the system to make alterations to its sub-processes or even gain new sub-processes that will be used in the future. Human beings cannot do much to change their individual selves, for example they cannot ‘learn’ to digest a poisonous plant that might become food. Biological evolution is needed to alter physiological and physical aspects of the body. Humans can adapt within narrow ranges of tolerance to changing environments, but not change their fundamental ways of interacting with the environment.

They can, however, change their tactical models, or in other words, learn to behave differently with respect to the world. For example, humans learned to construct and wear clothing to increase their tolerance to cold conditions, something necessary during the periods of glaciation in Europe and Asia. This was not a strategic decision but a tactical move that had strategic consequences for individuals and for the species. The only internal sub-processes that humans can alter or acquire are concepts encoded in engrams in the brain. Strategic decisions generally involve changing sub-processes, so an example of a strategic decision would be when deciding on what in the world should be attended to and be a source of learning. For example, when someone thinks about what career they would like to pursue they are making decisions that will result in changing their own brains (the engrams) one way as opposed to another.

Organizations, on the other hand, have tremendous ‘morphing’ abilities (called evolvability). They can alter, gain, or discard sub-processes at will (of the strategic thinker) providing they have the resources (i.e. financial) to do so. An organization's behavior can be radically altered in the sense that it becomes capable of doing something entirely new vis-á-vis its tactics and logistics. But organizations are limited by the cognitive capabilities of their strategic managers (CEOs). Unless they possess real vision and knowledge of the world, they may not be able to take advantage of their evolvability. Most organizations change as a result of experimentation and muddling through rather than planning and executing. It is tempting to wonder what might happen if an organization actually did do a good job of strategic planning and execution. I think there are a few examples in history, but since those successes depended on visionary individuals, after they retired or moved on, the organization generally fell back into the muddling mode.

The Trouble with Markets

Really complex systems, those with many various kinds of sub-processes that must work together to be successful in a complex environment, require a cybernetic hierarchy in order to do so. One of the reasons this is so is that inevitably communications break down between more distant sub-processes when the information must be relayed across many such intervening sub-processes as is the case in a highly connected but sparse network such as depicted in figure 10. Here all nodes, representing sub-processes, are fully connected through interactions (abstractly represented by links between nodes). Any behavior changes in any one of the sub-processes can be propagated throughout the network eventually impacting all the other nodes and probably with reverberating effects at that.

Figure 10. In a network of interactions, even if sparsely connected, the propagation of effect due to one node may affect every other node.

There will be time delays inherent in the system since it takes time for information to propagate. Add to this the notion that active (thinking and deciding) agents can exert local control over how and when (or even whether) the process can propagate the information to other nodes and you have the potential for the introduction of noise and distortions. The results can be disastrous when time delays and positive feedback loops (amplification) are involved.

It turns out that markets are really like this situation. Markets are systems in which aggregates of agents transact (trade) through links. Such transactions can be simple exchanges such as barter or more complex, involving tokens of value that are exchanged along with the movement of goods or performance of services. When markets as systems are relatively simple, e.g. the outdoor markets where food and trinkets are traded the possibility for establishing connections between agents for the purpose of establishing the relative value of what is being traded makes it possible for the market to establish consensus values in the form of fair prices. Each seller knows what effort and costs went into the production of their products and each buyer can attempt to keep the sellers as close to those costs, i.e. not expect to make a huge profit at the buyer's expense. Through the jostling of buyers and sellers haggling the magic of a market mechanism for setting prices is pretty much what Adam Smith described in Wealth of Nations.

Unfortunately as trades become more complex, for example involving transporters and middlemen, as the separation between buyers and sellers increases in time and space, as products and services become more complex and specialized, the information flow needed to allow that magic to work is increasingly subject to distortions (including false or misleading information). Prices fail to reflect any real costs (plus fair profit) and the system becomes vulnerable to crashes and bubbles — exactly what we see in the modern world.

Even though libertarian ideologues clutch onto their cherished Randian notion that the market is the best mechanism for establishing prices and, through competition, generate greater goods and services, systems science shows us this is not the case. Market mechanisms are good for small-scale and simple systems. But as the system becomes much more complex they are incapable of providing the needed information flow to ensure all sub-systems are working optimally.

Throughout the history of life on Earth, from its origins to the present time, nature's answer to emerging complexity has been the same over and over. At a certain point in a system's evolution, hierarchical control evolves so as to stabilize and maintain the system. If it does not, the system fails to survive and therefore cannot compete against those that do. The latter are the survivors (and replicators) and live on to undergo further evolution and participate in further complexification. Societies and their economic systems are no different. Governments have always developed ways to regulate flows in systems through various supra-economic mechanisms (e.g. taxes and policies). They are (usually) unwittingly carrying out the natural emergence of hierarchical control, albeit messily. Evolution does not work by producing perfect systems, in the sense of engineering for a purpose. It experiments. It is a massively parallel search through a possibility space of options and only some of them work. The history of humanity is full of multiple attempts to find some mechanisms of regulation (operational, coordinational, and strategic) that suit their societies given the technologies and beliefs held by those societies.

None of the societies to date have gotten the formula right and that definitely includes the western democracies. The combination of republican governance (i.e. representative democracy with evolving inclusiveness) and capitalism, along with, as it turns out, foolish beliefs in the role of greed and profit taking, were bolstered in the late 19th centuries by the unprecedented influx of high power energy in the form of fossil fuels that were abundant and relatively easy to extract. These interacting aspects reinforced one another to produce copious material goods and services, what I would call ersatz wealth. Those agents who benefited most from this explosion of production naturally looked upon the ideologies behind the aspects and concluded that it was good to let capitalism and free markets flourish in a society of free individuals. They didn't think twice about the role of energy, so distanced were they from the realities of existence (Figure 1) that it never occurred to them that wealth was not a product of capitalism and free markets, but in reality a product of energy flow. It wasn't until the latter half of the 20th century that some people started questioning this situation. What would happen when the finite resources of fossil fuels became depleted? The modern meme holds that technology will figure out a replacement.

In my next post in this series I will address the issues involved in designing an intentional political economy based on hierarchical cybernetics and biophysical principles. Where this goes involves recognizing that the biological mandate, as described earlier, must be regulated in such a way that agents can be free to act in manners that maximize their well-being but also do not jeopardize the well-being of the society (indeed the species). It is hard to imagine that a governance system comprised of human agents, with their faults and foibles and also driven by the biological mandate, could achieve the right combination of benign oversight and non-coercive regulations needed for the complexity of modern human societies. But it seems a worthy challenge to me to at least ask the questions.

June 21, 2014

It's the Solstice, one of my five days of reflective observation, so a happy holiday to all.

What I tend to reflect on with the Summer Solstice is peaks. It seems we are seeing a lot of phenomena associated with civilization either approaching or already at, or even past, peak. Peak conventional oil production, already past, will soon be followed by peak non-conventional oil (and gas) given the costs of extraction and the effects of peak capital on keeping up the rate of drilling. We've seen peak middle class expansion - now in contraction. This one is due more to the malaise of the economy in general due to the long past peak net energy per capita, but aggravated by the still increasing income disparities between the top 1-10% and the rest of us. We have certainly seen peak governance effectiveness, probably reached its peak in the US during George W. Bush's presidency. Elsewhere in the world we've seen peak effectiveness and rapid decline in the MENA region. Europe has probably gone through peak economic growth and is now in decline.

Nevertheless we soldier on. I've been through my own personal peak - railing at it all. Now it is just sitting back and watching the inevitable decline take place. Who knows how long that will take.

But I'm serious about the soldiering on. For those readers familiar with my book project, I'm happy to report that it is in production as we speak. The publisher is going to try to get it on bookshelves by fall - perhaps by the Equinox! If any of you know of a university or college that might be interested in adopting it for a course, I'd appreciate an e-mail.

Now I shall go enjoy the sunny day we have, this solstice, in the Pacific Northwest.

The Origin of Self

I have been developing a concept of human consciousness as it relates to sapience. I am following ideas put forth by the neurologist and neuroscientist Antonio Damasio as given in the bibliography. But I have some additional thoughts to add, drawn from the works of other scientists pertaining especially to the research on wisdom as well as my own work on the hierarchical cybernetic model of brain. I have been using some of the same terminology as Damasio so that if readers want to follow up and get more details they can read his books and find some consistency between what I am writing here and his work.

Damasio's insights regarding the origins or sources of consciousness are particularly useful and take a different tack from the regular philosophical and psychological approaches. Damasio begins with the fact that brains are about life management, starting with the oldest, most primitive forms. Evolution has favored increasing management efficacy because it provides the possessors an increased fitness, i.e. they can exploit more complex environments if they have more complex information processing ability and increased behavioral flexibility.

But with increasing brain complexity comes the need for the hierarchical organization of the control functions. The modern human brain represents the epitome of a hierarchical cybernetic system, complete with high-level planning (strategic management), very complex tactical and logistical coordination, and, of course, a panoply of highly adaptive operational level functions. And this latter level is where Damasio starts his story of the construction of consciousness in the human mind[1].

His main argument is that consciousness is built on three basic constructs: wakefulness (alertness, etc.), mind (the structured and unstructured processing of images), and the ‘self’ (an on-going narrative of the state of the individual's biology) (Damasio, 2010). In particular, the first emergence of a self (as I described in the first post) is based on an ability to differentiate between sensory inputs that originate in the body or are caused by the body versus those that originate from outside the body. The very first thing a brain has to be able to discriminate is between those sensations (stimuli) that are caused by non-self and those that are caused by self. In addition, however, the brain maintains what Damasio has called a ‘map’ of the internal milieu of the body, the conditions in the viscera and blood that are linked to homeostatic mechanisms. The brain monitors these conditions and manages the response mechanisms, such as hormone and neuromodulator secretion. It maintains a continual mapping of body states as they change and responds accordingly. This level (operations) is basic cybernetic feedback control.

The most primitive brains maintain these active maps or what I have been also calling the models[2]. In this case the brain has a genetically endowed model of the body with sensory inputs from all parts of the body ending up feeding into the model of the body which is continually updated. The model contains reference states (ideals) that are used to compare with the current state and generate homeostasis responses as needed. But what is left in the brain is an ‘image’[3] of the body state at each instance. In the most primitive of brains that is the end of it. New states generate new images. But in evolutionarily more advanced brains these images are actually available to higher-order maps (see Figure 2 in Part 1 - Exploring Consciousness) where multiple sensory images are integrated in order to produce more complex behavior actions. These higher order maps may maintain something like working memory, or at least some kind of short term memory traces. They are also subject to modulatory inputs (e.g. neuromodulators from the homeostatic mechanisms) that can affect the further processing, acting as feedback to this level from the lower levels. The residual pattern images are part of a coordination level of cybernetic management. Their persistence over slightly longer time scales allow the spatial-temporal integration of patterns that can then provide coordination feedback to the lower levels. Damasio has described this phenomenon as the basis for what he calls “feeling”. It may be hard to imagine a fish, for example, having feelings. But this is because the feelings that humans have are at a higher level and being interpreted by consciousness. There are multiple levels of a self that have respective levels of feelings.

The primitive parts of brains, no matter how advanced, generate what Damasio calls the protoself, the primitive self that worms and fish experience. In Who is I? I provided some aspects of Damasio's extension in the theory of self, showing how more complex brains produce a ‘core’ self, the first level in which a self also is aware of itself being affected by the environmental situation. From there the most complex brains with extensive neocortex, having memories of both the biographical past and the projected future (plans and imagination).

The Strategic Self

Damasio gives a good account of how the biographical self emerges as a “protagonist” (his terminology) with a private sense of agency. His argument for the evolutionary fitness of biological management of the self in an increasingly complex world, likewise, sits well. The autobiographical self embedded in a mind that is extraordinarily competent at manipulating abstract mental models is clearly advantaged in survival and reproductive success in a world of so many ecological opportunities. Humans were obviously successful in invading and exploiting almost every ecosystem on the planet.

Human consciousness is not merely autobiographical. It includes a planning model above the super-observer model shown in the Who is I post. This model answers the question, “What should I do?” But the question being asked is not simply what should I do next (short-term). That is a tactical question. Rather the planning model extends the time, spatial and social horizons greatly. The time horizon can extend over years. The spatial horizon can extend as far as the mind has been exposed to stories to foreign lands, even to alien worlds. And the social horizon extends to not just kith and kin (the tribe) as cooperators, but to perfect strangers as long as there is a context for association.

Members of the species Homo sapiens think strategically in the sense that they can consider all of the past that they have informational access to, the current situation (also that they have informational access to), and with a sense of desired outcomes in the future, they can lay plans of action to get them to those outcomes. This can be done consciously, of course, with some effort. The farther the horizons anticipated, the more effort required. But somewhat ironically, the vast majority of humans do not really think strategically in a conscious fashion. Their brains are still engaged in planning, but they are doing it subconsciously and only become aware of the results as their intuitive judgments bias them to act in ways that tend to further their plans. Their tactical management thinking, that is: “What should I do next?” is what they are aware of and their intuitions provide the answers from deeper in their strategic management brain. The vast majority of people muddle through life unaware that their brains are trying to keep them alive and in the best possible situations.

For the vast majority of people in the world strategic thinking is not foremost in their minds because by the time they reach adulthood they have settled into something of a routine. As long as their environment does not really change that much they do not need to lay additional plans beyond the limited horizons they have already mastered. Strategic thinking, for most people, is something they do more as teenagers than as fully developed adults. We often call it daydreaming or imagining.

On some rare occasions an individual is born with a tendency to maintain a youthful mental condition that allows them to continue dreaming long into their adult lives. These are the explorers who wonder if the future would be more rewarding somewhere else. Among this lot, but rarer still, there are individuals who do not merely dream and wander in exploration, but people who consciously ask questions about the future and distant locations and peoples. These are the true conscious strategic thinkers. They are not just explorers, but intentional explorers who are seeking more information, better knowledge, and looking for the best solutions to living in a changing and dangerous world.

Such people experience a much broader (bigger) form of consciousness. Their considerations of the future are not merely fantasies (wishful thinking about the future), but realistic assessments of what is likely to happen in their world that will impact them and their kin, and what they should do about it in advance. They too have intuitions but their judgments take so much more into account and are better organized by broad systemic knowledge that they are more often veridical (i.e. correspond to what actually ends up happening) than the average person's.

The strategic self, whether operating in consciousness or just below the surface, might seem to be the epitome of self and is more or less where Damasio ends his model except for noting that consciousness itself is not the epitome of human cognition. He views ‘conscience’ as occupying a higher level than mere consciousness. After all, the worst criminal minds are conscious but clearly lack a conscience.

Conscience is what makes us more humanistic. In my model of sapience I point out that our judgments are as influenced by moral sentiments (cooperation, altruism, empathy) which are the basis of our social nature. We need not be conscious of our conscience in the same way we need to be conscious of our rational thoughts and, indeed we rarely grasp exactly why we perform acts of friendship and kindness other than to say it is our nature. We can become aware of emotions that attend these acts as well as, of course, negative emotions. When we do something wrong we feel regret and possibly shame. But these remain just feelings. What a strong strategic mind will do under these circumstances is re-analyze the conditions and acts that led to these feelings and think about how to correct for mistakes (how to try to be a better person in the future).

So, my theory of sapience and Damasio's model of consciousness + conscience seem to be complementary and cover a lot of ground in trying to explain the brain basis for what we humans experience as subjective reality. But, as I indicated at the end of the last post, there seems to remain one more aspect of awareness that is not yet accounted for. The planning model of figure 3 in Part 2 provides the basis for a strategic self. But, as I argued above, the scope of the various dimension horizons, is generally limited and most people do not really consciously plan out very far. The answers to the question, what should I do, are bound by constraints due to the limits of tacit knowledge that most people have. However, the same brain region that provides the basis for this planning model can be even further expanded in some individuals so that their horizons are far. In Figure 1 I show an expanded planning model.

Figure 1. The highest level cortical map that resulted from the expansion of BA10 in late human evolution provides a hyper-consciousness feeling. The new map extends and expands the planning map of earlier species of Homo.

The Ineffable Self

As with conscience there is no need to have the expanse of this model in consciousness for it to have its effect on the basic planning model processing. This particular model takes the questions of who have I been, who am I now, and who shall I become to some much greater scope. The figure shows the ineffable self wrapped around and integrated with the planning model because it is an extension of the latter, but with very different processing properties. The reason I call it ineffable is because one senses that there is something real there, but cannot observe it as a consciously held object. The protoself was observed by an observer model that interpreted what was happening as the organism's body states were affected by the environment and sensing what that environment was. This observer gave rise to the core self in Damasio's terminology. In turn the super-observer model observed the changes in the core self and combined that situation analysis with the autobiography of the individual stored in memories to produce the autobiographical self, a being with a private experience of agency and ownership of thoughts. The feeling of self culminates in humans in not only an ongoing narrative of the self living in the world, but as a story in abstract symbols of that narrative and hundreds of parallel narrative about what else is going on in the world. The use of language allows a highly efficient mental capacity for dealing with extraordinarily complex worlds.

The next higher order model is one that takes all that comes from the biographical self and transforms the narrative of past to present into a narrative of the future. As discussed above, sometimes this is explicit in conscious thought, but more often it is running in the background, so to speak. Nevertheless its effects are felt and observed in the super observer in which self consciousness operates.

With the expansion of the planning model to greater horizons something interesting happens. The effect of this expansion is felt in consciousness but only as an ineffable sense of a super-I, an ultimate I that is observing all that came from below but cannot cause the super-observer to form words to express what is happening. The super-observer is not observing that higher-order mental capacity. It receives influences (intuitions) from the planning model, but the effects of the “Grand Ineffable Sense of Self” are not observed directly. They are only experienced through the planning model's effects on the super-observer. This, I claim, is what makes the subjective experience of consciousness seem mysterious and unexplainable. Each one of us knows there is something else there besides our direct thoughts but we have no observational access to what it might be. So the easy thing to do is imagine it as a separate spirit, a soul. Descartes' dualism fell victim to this, but so have all of the more spiritualistic explanations over human history. We all have this private experience of something being there but we cannot see it in any direct sense. Until the age of brain science we are currently in, there was very little else we could think. Introspection would not expose it since only the super-observer could produce analysis of what it observed and the grand self was not in its view.

Of course this is not the definitive word on how the brain produces this thing we call consciousness or why some aspect of what we experience seems to defy description. The model I have presented is (hopefully) reasoned conjecture on my part based on several different but converging lines of neuropsychological research. Below is a general bibliography of reference works that I have tried to assimilate to construct this model. The study of the brain and its workings and evolution is an incredibly intellectually stimulating exercise. I heartily recommend it to anyone.

Where to Next?

In modern systems analysis one generally starts with an attempt to understand the whole system as a black box in order to situate its purpose in the grander environment in which it operates. You map and parameterize its inputs and outputs, you observe its behavior. Then you start to decompose the system into its constituent subsystems, and them into their constituents recursively until you reach a natural stopping place for analysis. This is called top-down analysis. Understand the whole before you attempt to understand the parts. The reason I have been mucking about in consciousness studies is that I am trying to get a handle on the top level of human mentation in preparation for such a top-down analysis of sentient systems. Such an analysis can lead simply to better, deeper understanding. But it can also lead to ideas for designing artifacts that emulate the natural systems.

In my PhD research I embarked on an agenda to emulate natural intelligence (not worrying about either consciousness or sapience!) in a machine. My strategy was to approach it from an evolutionary perspective. That is I started by looking for ways to emulate very primitive brains that showed the capacity to adapt to changing environments[4]. I succeeded in emulating what I came to call a “moronic snail” brain in a mobile robot platform (MAVRIC). I was successful in getting some publications out on how I did it, but the field of Animatics (the simulation of animal-like behavior in robots) came to be dominated by the then-new non-traditional areas of artificial intelligence such as artificial neural networks (ANN), fuzzy logic, and some Bayesian statistical learning methods. These methods were showing some interesting first results and had the benefit of being functionally (and mathematically) easy to understand. My approach of emulating brains by simulating very low level functionality of real neurons (namely synaptic plasticity in multiple time domains) was ignored and I got discouraged (as an academic). Fortunately my work had been early enough to have garnered the necessary attention to support my granting of tenure.

I had been sitting on the sidelines as far as this research was concerned. Indeed I ignored the area because I found ANNs, for example, to be completely boring and never believed they would ever lead to the kind of sentient intelligence that remained the prize of AI. My robots had sat on the shelf for years while I went off to study energy, sapience, and then consciousness. And, wouldn't you know it, while I was ignoring the field several researchers finally started coming to the conclusion that the dominant paradigm in animatics was likely a blind alley. Concomitant with this dawning realization neuroscience had started unraveling the way in which concepts are organized and represented in the neocortex. The story is complex, but it turns out that the main premise of representation in ANNs was wrong and my neural models were right! That started me thinking about the problems again.

So now, several decades later, I have a big picture of what cognition looks like. I have worked out some details of how a hierarchical cybernetic system works and have developed a scheme for how to construct a brain using that and my version of neural simulations. So I am off to the research races again.

Back in Jan. 2011 I posted a brief on Brain Complexity at Multiple Scales in which I ran over the various size and time scales in which relevant cognitive processing took place. This followed the evolutionary strategy from simple to complex, but also previewed the program I intend to pursue. Having already done a simple invertebrate brain I now seek to invade the cognitive space of reptiles! In some future posts I plan to outline the steps I will be taking. The ultimate goal is to construct a neocortical framework for building mammalian-scale brains. This is now in the realm of feasibility given the newest generation of microcontroller devices, sensors, and actuators for robots. I plan to follow Damasio's framework of giving my brains access to more complex body states so that they can incorporate a protoself and core self (the reptile). With the addition of a neocortical framework I hope to develop a capacity for an autobiographical self in a machine.

Here is why I am pursuing this now. Human civilization is about to collapse. We may end up in a new dark age, we may even go extinct. Or we may salvage some semblance of civilization but only for a few survivors. If the latter turns out to be the case humanity is going to need some help cleaning up the mess. Assuming we find some way to power them, really intelligent robots with consciences might help out.

It is possibly a Quixotic dream of a foolish old man. I'm sure many will see it as so. But when we stop dreaming is when we stop being human altogether.

Footnotes

[1]. Damasio allows that all creatures with brains have minds; that the mind is not the same as consciousness, but rather the result of continuous processing of sensory-driven imgages in those brains.

[2]. A model is any representation of the “thing” under consideration, the subsystem of interest. A map is generally thought of as a more static representation of a relation between two different “domains” (technically between a domain and a range, but I'm using the word domain more generally). However, a model is a dynamic maping, one in which the interally represented relations can change with changing associative conditions. Models can be used in control systems, e.g. what is often called a ‘plant model’ is a transfer function that maps control signals to the actuators of the plant from the sensory signals that measure the plant state or output. For my purposes (and I think Damasio would concur) map and model mean essentially the same thing.

[3]. In this usage, an image is a pattern of roughly synchronized neural firings across a specific assemblage of neurons in a network. These firing patterns represent an image that is active in mind. When the system is not being stimulated (e.g. from below by sensory inputs) the images reside in dispositional form (Damasio's term) or in passive storage as long-term potentiation of the memory traces associated with that specific pattern. Only activate assembleges form images at a given instant.

[4]. The evolutionary strategy might first look like a bottom-up approach but in truth it is what I call a “piece-wise” top down approach. That is as the system at a given stage of evolution is decomposed and understood the knowledge serves as a basis for what to look for when you start decomposing a more evolutionarily advanced system. So starting with invertebrate brains has given me the first insights into the neural processing of self vs. non-self (Figure 1 in the first post). It also gave me confidence to tackle a more advanced system like an early vertebrate brain (e.g. a fish), which is what I am working on at the moment.

April 19, 2014

Other parts of the Series:

The Semantic Trap

It seems nearly impossible for a writer tackling the consciousness problem to avoid a linguistic trap. Ultimately, when we describe consciousness as an act of ‘observing’ ourselves in the act of observing the environment and our physical states[1] we are faced with a seeming paradox. I am observing myself observing the world, but who is the “I” doing the observing? Is there an “I” observing the world? Is there another “I” observing the first “I”?

Douglas Hofstadter, in his 2007 book, I Am a Strange Loop claimed that these seemingly multiple “Is” were really one and the same observer and that the observer is a self-referential entity that is observing itself observing.

On the other hand, Antonio Damasio, in The Feeling of What Happens has constructed a layered architecture in which a series of “maps”, e.g. cortical and sub-cortical structures that process images act as observers of the layers below (see figure below). That is, information about what is happening in the world and in the body passes upward toward layers that will use this information, along with knowledge encoded in memory and innate dispositions (from lower levels) to decide what actions should be taken. This is basic consciousness, or what Damasio calls “core consciousness.” Much of the functions contributing to core consciousness are performed in the more primitive parts of the lower brain. The five lower maps shown below are formed in various cortical areas in midbrain and paleocortical structures such as the amygdala and hippocampus. The two higher level maps were possible as soon as structures such as the cingulate cortex could encode experiential knowledge and provide comparative analysis between what is happening and what memory predicted would happen.

Figure 1. From Figure 3 in prior posting.

The evolution of the neocortex in mammals added a spectacular capability in forming much more complex maps as well as greatly expanded monitoring of the biographical self. The neocortex is a massive memory system (c.f. Fuster, 1999) that allows an animal to build a historical record of experiences and how those experiences affected the organism. This memory system is actually a learned model of how the world works and how the organism works in that world. In figure 2 I show what I call a “Super-observer model” that is dependent on neocortex sufficient to integrate experiences into the developing model. The new map/model[1] is capable of asking more advanced questions regarding how successful the organism has been over its lifetime at dealing with and adapting to the environment it has been interacting within.

The organism now maintains a more explicit sense of the self. The model that is constructed within the neocortex includes a model of the self. Most of the framework of the model comes from the core self, the brain's inherent and generally fixed operational, basic logistics, and basic tactical models (e.g. the amygdala monitors for threats in the sensory fields). But the neocortex-based model allows for incorporation of learned experience into that framework. For example the amygdala might be leery of any snake-like (slithering) thing that it detects. It would initiate a response in the form, usually, of getting away. Evolution “trained” this structure to recognize and respond to snakes because, over the history of the species and its predecessors, snakes have proven to generally be dangerous (circuits in the amygdala that triggered safety responses were selected for, those that didn't got their possessors bitten!) But with a neocortical model, the organism might learn that not all slithering is done by snakes and so even if the amygdala tries to initiate escape, the early prefrontal cortex could check the model and re-check the source of the trigger and decide to not escape if it isn't warranted. This is how dogs and cats can learn to be friends!

The self model in neocortex is far more malleable than the core model. It can override the core model (as just explained) under appropriate conditions and this has tremendous fitness benefits for animals that explore larger and more complex environments for increased opportunities for food and mates. The core model is course-grained. It treats many different objects as threats or opportunities that really aren't in the larger context. If you responded by running away every time your amygdala detected what it thought was a threat you would miss some opportunities that could contribute to your success.

Figure 2. The expansion of core consciousness depends on a higher-order map that has access to a biographical memory.

Mammals and many birds behave as if they had a sense of self. That is they do not merely react to stimuli in pre-programmed ways all the time (as a reptile generally does). They show the ability to problem-solve in novel situations, such as getting a food morsel out of a bottle[2].

A sense of self comes from monitoring the impacts that things happening in the environment have on the organism's body. Every action-result pairing has some affective impact, such as when food is successfully obtained the ‘pleasure’ associated with the action to get it has a reinforcing effect on learning that pairing. Rewarding stimuli-action pairs makes those more likely to be chosen in the future. Similarly punished behaviors are made less likely. Damasio (1994) describes what he calls “somatic-markers”, or conditions of the body state concurrent with the mental images being processed, where affective (emotional) states can be associated with newly formed memory traces so that in the future the activation of those traces can be influenced by the emotional content. For example a memory that was formed under negative emotional states may affect a current condition decision if that trace is re-evoked in the present. The possessor will tend to avoid whatever triggered the recall.

The Great Leap Forward

Primates evolved more extensive prefrontal cortical areas that were larger proportionally to the rest of the neocortex (for the size of the brain). The prefrontal cortex sits at the very apex of the behavior decision processing. It has been said to perform the “executive” functions, which includes things like directing attention, calling relevant dormant memories into working memory, even just deciding what needs to be decided. These brain circuits emerged as management specialists to coordinate the rest of the neocortex in their functions. Fuster asserts that they evolved from more primitive motor cortex in the frontal lobe and are, in fact, specialized action (motor) cortex. But the multiple coordinators needed to be coordinated among one another. For example there is a circuit that is responsible for evoking some functions in the cingulate cortex that will make comparisons between items in sensory vs. working memory. Based on the outputs of the cingulate cortex another patch of circuitry decides how to integrate the new sensory information into the exiting model (active in working memory). These circuits need to be coordinated in action and timing in order to produce the proper sequence of processing steps.

The precursors for these circuits already existed in older frontal lobe cortex. The “Super-observer” not only observed what is happening now relative to what has happened in the history of the individual, it also became responsible for considering alternative behavior options in order to add more exploratory possibilities to the repertoire, Rather than be completely dependent on already learned behaviors, mammals evolved increasing ability to try new possibilities. This required a higher order judgment capability than the simpler comparison analysis provided by the older cortices. This too was accomplished in the prefrontal circuits that sat right at the right point of convergence of all the information flows.

The patch of tissue in the prefrontal cortex, right at the very apex, right behind the eyebrows (in human skulls) evolved as the “final” model/map that became responsible for coordinating all of the coordinators (the rest of the prefrontal cortex). To do this the capacity to ask what alternative behaviors might be tried needed to become a more organized and motivated function. It had to coordinate all of the other resources of the brain to formulate plans for future activities. The organism is always driven by biological needs. These are the motivations that drive the organism. The long-range plans (which need not be conscious, ironically) have to support meeting these needs.

Consider, for example, the biological mandate to reproduce. We humans, and presumably all animals, are not consciously aware of a drive to reproduce. Instead there exist subtle and powerful drives to have sex, which provides rewards to the organism (dopamine shots!). But humans construct elaborate models of love and bonding relations that constitute their basic ‘plans’ for ultimately consummating a fertilization event leading to one or more children. All of our conscious thoughts about love and mates, how to choose the ‘right’ one, etc. constitute our plans and whatever they turn out to be (and they can be incredibly variable in content) they will drive us to make choices in daily life that should lead to success in reproduction. That is, if the plans we constructed are realistic. A major problem with the human condition, being subject to the developmental (learning) influences of a complex culture, is that too often, especially in modern life, those plans are essentially unrealistic. An example is the Hollywood version of love and ‘happy ever after.’ Most people (present company not excepted!) have tremendously wrong visions of what marriage will be when they achieve it. This is a major downside to being conscious and non-conscious simultaneously.

Figure 3. Part of the prefrontal cortex adds a new kind of map/model that works on planning longer-term courses of action or “strategies.”

The sense of having a purpose emerges from the construction of a planning model and the on-going monitoring of the execution and success of the plan produces the sense of agency — the self power to exercise control over the self's situation especially by controlling elements of the environment.

Too often the plan simply isn't viable. It does not represent the real world, or the real self, as they actually are. And this can lead to a major disconnect between expectations (to carry out the contents of the plan) and actual results — that sense of power over the environment is thwarted. The frustration that builds up internally as a result of continuous disappointment, whether consciously experienced or not, leads to mental breakdowns sooner or later. Our modern culture seems to be particularly good at influencing the construction of unrealistic plans and unrealistic beliefs about how the world works and who we are as persons.

The smallish patch (designated as Brodmann area 10, BA10, in brain area maps) isn't a homunculus. It does not accomplish all of these functions and be the seat of the memories of the senses of purpose or agency. It is just a super coordinator whose task is to coordinate all of the rest of the brain components to accomplish these results. This model of the brain and the functions of regions fit the hierarchical cybernetic model of operational, logistical, tactical, and strategic management (see my post: The Science of Systems 7. The control is over the vast memory stores in that hierarchical fashion. BA10 coordinates existing prefrontal areas for long-range plans and monitoring. They, in turn, coordinate multiple other areas in association and pre-motor cortices.

Human consciousness, at its best, involves a brain actively observing its own working, which is primarily the organization of memories on the basis of prior experience and current situations but mediated by memories of the future (plans). But this level of consciousness is not always active. Most of our lives are governed by current perceptions, especially when we are concentrating on accomplishing specific tasks. We only enter this consciousness of consciousness when demands from the external world are not keeping us focused and responding.

Over the last one hundred thousand years (or more) human evolution has seen a tremendous jump in the size and connectivity of BA10. One of the most important effects that may have been a result of this is the great increase in level of judgment, that is judgments over much more complex and longer-time scale issues. Additionally human empathy and altruism have increased in concert with the expansion of BA10 (and judgment). This is what I have called “sapience.”

Consciousness of self and of self being conscious is the new capacity of the human brain, evolved from precursors in mammalia, primates, and hominid genera. Humans are a new kind of animal and are potentially positioned to become a hyper-social being, not like ants or even naked mole rats, but wholly unique in how they can organize their social conditions and their lives. However, it is looking like that will take more evolution and more time than we may have available..

Observing the Observer Observing another Observer

The hierarchical structure of the brain presented here suggests that rather than a recurrent loop (Hofstadter's strange loop) giving rise to the ‘I’ in my mind, that there is something like an ultimate observer that also acts as a very long time-scale coordinator, a strategic planner that is also constructing an extended model of the self which is the basis for the sense of ‘I.’ However I think Hofstadter is also right! In my next attempt to try to understand this phenomenon of consciousness I will explore how language supports the reentrant process of the self talking to the self, narrating the autobiography of self as it develops. That autobiography necessarily includes the narrative of the environment experienced as well as an interpretation of the self and its reaction to the environment (as well as to itself). In this sense I am a strange loop that results from an evolved hierarchy of cybernetic controls.

I am also interested in the way in which we empathize with others by creating strange loops that allow us to vaguely be another person. You are a strange loop that I can kind of understand.

Footnotes

[1]. A model is a dynamic map, that is a mapping from inputs to outputs that changes with the incorporation of new knowledge and feedback from experience results.

[2]. Actually a lowly octopus can do this trick even when there is a cork in the bottle. However, the work that the octopus does appears to be largely trial and error efforts rather than reasoned based on prior experience. Octopi have some longer-term memory retention in that if they are presented with the same problem a few hours later, their time to completion goes down. But several days later they are back at trial and error as if they had forgotten the prior lesson. And, they had. Contrast that with a crow who upon discovering a method for getting a food morsel out of a container, will retain the memory for months, if not years.

April 13, 2014

The Multiple Threads of My Interests

The last several months have been really exciting for me. The book project is wrapping up and we should be getting the final manuscript draft to the publisher in early June. But several other areas in which I have been working are beginning to come together, to converge, as it were, in a grand synthesis. I must admit I have been startled by what is happening. I think, in part this is due to my stopping my obsession with how the world is falling apart due to human stupidity and going back to my roots in research in systems science. In any case the multiple threads of my interests are starting to weave together in what is, to me, a profound tapestry. I will try to explain.

Back in Feb. 2009 I wrote a blog post inspired by an e-mail question from a reader. I called it “Subjects of Interest.” The reader was puzzled about my penchant for writing about so many seemingly disparate topics such as energy, brains, and economics. I tried to show that these subjects are actually all interrelated and form a larger scope system; that I had used my understanding of systems science to successfully explore each subject from that perspective.

Over the years that I have been writing Question Everything I have delved into a fair number of subjects that were either explicitly systems oriented, or implicitly so. Here is a list of the topics in which I have done some pretty active exploration.

Brain Science in general, but especially the functions of the prefrontal cortex in consciousness and sapience

Information/knowledge theory and hierarchical cybernetics

Auto-organization, emergence, and evolution (esp. the origin of life problem)

System dynamics and approaches to modeling systems with a new process-based semantics

What ties these all together is Systems Science (my textbook will show this). Up until just recently, however, I have pursued each somewhat independently from the other even while intuiting that somehow they were all part of a larger whole. Now, it seems, my intuition is starting to take more shape in my mind. A recent insight gotten from working on a research project with the artificial agent brain showed me how these all come together, how they can all be integrated, unified, into a single conceptual framework - a system.

In future blogs I will outline how I see this coming about. Right now I will just provide an overview and mention some of the ways to bring it to fruition.

The Brain is a Universal System Model Building and Simulation Machine

For several years I have been working on the design of a new modeling language meant to upgrade what is known as system dynamics, originally developed by Jay Forrester at MIT (also see Donella Meadows wonderful book, Thinking in Systems). Forrester's language relies on a semantics called “stocks, flows, and controls.” It is true that all dynamic behavior of a system can be simulated by developing a stock and flow model such as in Figure 1 below. The changes in flow rates and stock levels over time constitute the dynamics. These models are discrete time-based and simulated by repetitively computing the state variables at each time tick. Graphs of the output data then show the dynamic behavior of the system as a whole.

Figure 1. System dynamics (SD) modeling has been based on the use of stocks, flows, and controls as very abstract concepts. This shows the basic semantics of the SD languages. The way the component pieces hook up is the language syntax.

For me this kind of abstraction was useful for many kinds of “simpler” systems. But when I started trying to build more complex system models involving details of energy, matter, and message (information) flows the lack of specificity in flow designation started to become a problem. A flow of energy and a flow of material (not to mention messages) are subject to some different laws. For one, the Second Law of Thermodynamics requires that with every conversion of energy flow there is an unavoidable loss to waste heat of some of the energy, i.e. less work can be accomplished downstream of the conversion. Material flow is more constrained by the conservation principle. It often takes more energy to remove waste materials on top of this. I was unsatisfied with the treatment of messages, only seen in the thin arrows (called “influence arrows,” which represent the flow of information that affects controls (the valves in the figure).

Lastly, and very importantly, I wanted to be able to abstract all of these flows, stocks, and messages into an object called a “process.” What we see in systems are objects that take inputs and processes them into outputs, products and wastes. The products are generally used by some other process which is what gives the output value and becomes the raison d'être for the supplying process. The abstraction (in the sense of hiding all of the details of a system) would allow the modeler to combine multiple processes into meta-systems. In that way one can construct increasingly complex systems from simpler subsystems. I will provide an overview of this in a future blog.

The limitations of system dynamics semantics definitely showed themselves when I attempted to model a neuron and its synapses using it. My first neural/synapse models based on system dynamics were a good exercise and provided many useful insights, but I found the restricted semantics difficult. I ended up developing the model in the ‘old’ way — I wrote it in C. Even so, the experience with DYNAMO and system dynamic left me feeling that with a little more semantic support a language that allowed the creation of complex dynamic models would be a great “project”. I put it on the back burner because my interest in neural systems as a better approach to artificial intelligence had been piqued. For the next many years I concentrated on developing a more deeply biologically inspired model of a neuron with particular focus on the multi-time domain dynamics of synapses and their plasticity, which I had become convinced was the key to a really efficacious method for encoding memory engrams.

Fast forward from the mid-80's to about five years ago. When I had moved from Western Washington University in 2001, where I was making progress on the “Adaptrode” synapse model, using it in neural networks that controlled the behavior of a mobile robot, to the University of Washington Tacoma, where the hoped for support never materialized, my robot experiments came to a screeching halt. As a result I started casting about for something else to do research in. A few false starts later I went back to the system dynamic modeling language and started to coalesce my ideas into some concrete approaches.

As part of my desperate search for a new research arena, and hoping it might have some relation to my previous work, I had “stumbled” into the psychology of wisdom. I had also been studying the growing threats from global warming, population overshoot, etc.; all of the problems that beset mankind and threatened its very existence. In the psychology of wisdom I found a clue. The reason humans were consistently making stupid mistakes and had adopted a completely fallacious ideology (capitalism, profits for their own sake, and growth) is that they lacked the wisdom to make good judgements and hence good decision. Somewhere along the line my interest in brains and behavior along with this insight from psychology led me to a much deeper investigation of brain functions and their evolution to seek explanations for the human condition.

About the same time my curiosity about the state of our energy systems, namely the concept of “peak oil” was starting to drag me into a deeper study of that subject, and what I found made me once and for all realize that humanity was doomed by sheer lack of sapience. All fossil fuels are clearly, unambiguously finite resources that if you extract and use at increasing rates will run out sooner or later. Moreover the supposed substitutes (neoclassical economists insist there are always substitutes when the price gets too high), alternatives such as wind and solar, were highly suspect in terms of their capacities to provide the same level of power that our societies had grown accustomed to. I started using my evolving modeling language to try and model the net energy production of photovoltaic generation given that the energy inputs into the process of producing the cells and installing them seemed to me to be excessive. Net energy is all that counts in system dynamics and my preliminary attempts convinced me that chasing these alternatives to keep society going with business as usual was a fool's errand. I discovered the work of Charles Hall at SUNY-ESF on ‘energy return on energy invested’ (EROI) and given the deep implications for humanity, if my model was giving reasonable answers, I decided to study this phenomenon much more closely. I had a sabbatical leave coming up so I decided to go to SUNY and study EROI with Hall. My objective was to understand the math better, and to see if my new process-based language would be able to produce viable models of energy systems.

Long-time readers know the rest. What I found out in this background research settled matters in my mind once and for all. The world of human societies was doomed to collapse (while at SUNY I met and struck up a friendship with Joe Tainter, who had penned the infamous The Collapse of Complex Societies. Joe had been working on the thesis that collapse of prior civilizations had been greatly influenced by what he described as the declining marginal returns on complexity; that being the result of societies trying to solve problems, the solutions then leading to more problems. With his association with Hall, et. al., Tainter had come to see that the energy flow through the society was related to the complexity issue so he came to recognize that complexity can only increase and succeed if there is adequate energy flow to support it. The Romans, for example, ran out of energy and could no longer support their complex infrastructure (to put it simply).

After returning from SUNY I was inspired to increase my efforts on the modeling language project. I've had several graduate and undergraduate students working on bits and pieces but the whole concept is, itself, rather complex and requires many pieces working together. We've made some progress, but not enough to go ask for money yet.

Meanwhile I had all but forgotten the neuron modeling as I pursued these other lines of interest, The seeming inability of the NN community to understand my models had more or less left me cold for struggling with pursuing the concepts furter. Then, two years ago, roughly, I came across a research paper that signaled something profound. It seems that in the years I had been away from neural network research a fair number of researchers had discovered the problem that I had tried to articulate to the neural network community without much success back in the early '90s and had been working on with my Adaptrode model; that of learning and forgetting non-stationary relations. The Adaptrode works by encoding memory traces in multiple time domains (i.e., real-time, short-, intermediate-, and long-term traces). In a future blog I will provide more, non-technical details about how the Adaptrode model solves some pretty serious problems in memory trace encoding in the real world.

With that kick I realized I needed to get back in that game. I had the solution to the problem and now that the community was paying attention I might actually be able to make some progress. For the past two years I have been steadily pushing my previous work to incorporate a number of new ideas regarding how to actually simulate a brain of greater complexity than that of a ‘moronic snail’, namely, how to construct a neocortex structure to do much more advanced processing than I had accomplished previously. This was greatly accelerated by reading Jeff Hawkins' amazing book, On Intelligence. Hawkins had independently hit on some of the same principles I had developed in my research years before, which gave me confidence that I had been on the right track. I've revived that research agenda and have several graduate students starting to rebuild my simulation software.

The cerebral cortex (neocortex in mammals) is an amazing structure for capturing causal relations at multiple scales of space and time. It literally is a machine for constructing complex models of the systems it encounters and using those models to anticipate the future. Hawkins and many neuroscientists studying the functions of areas of the cortex in behavior and thinking have all come to very similar conclusions. As yet, however, I have not seen an explicit discussion of the role of multiple-time domain encoding in how these models are constructed and validated (i.e. committed to long-term memory).

Realization

Within the last several months realization that I had been working on the exact same concept but under different guises all along dawned on me. Actually I have to confess it came to me during a lucid dream one night! I woke up realizing that my modeling language, my brain model, my attempts at modeling systems energy flows, and my grasp of higher brain functions were actually all one and the same. If I could successfully build a deep model of a brain (especially the neocortex with a human-like architecture) that simulator would do implicitly what I had been trying to do explicitly with a modeling language. I would be able to let such a device ‘learn’ the world — that is, construct a model of the world from experience. It would be able to simulate the world it had learned to project anticipatory scenarios, having the same efficacy as humans. And maybe more so.

It is still unclear to me what value it might have to achieve a breakthrough at this late date. The world of human civilization based on extreme power is coming to an end sooner than we previously thought possible. The latest news on global warming and climate change indicates that the major impacts from this phenomenon are already underway and will accelerate in the years to come. So solving the problem of emulating natural intelligence in machines may simply be coming too late to be very helpful. Even so, my attitude has always been one of curiosity and discovery. As selfish as that may be, I have been motivated by an incredible need to understand things. Some time back I thought that was because I had the conceit to believe if I understood the way the world worked I could do something to correct the mistakes. When I was younger and naive that seemed like a reasonable thing to think. Now that I am older, recognize the foibles of humanity, and my own lackings, I realize that it doesn't matter what I do. My contributions, if any, will not change the course of our fate. Even so, I am still intensely curious. So I will continue to research the subject. I am able to see how systems science, modeling ‘language’, energy flow, evolution, and everything come together in the way the brain works and that will be my focus from here on. I will try to outline these disparate threads in future posts, and show how they all come together in a single consistent understanding.

April 01, 2014

Scientists Find a 'Hole' in Most Human Brains

I thought I would share this with readers. I have been busy working with some neuroscientists to test a hypothesis regarding the lack of sapience in the majority of human beings. This is a pre-release version, which you should be reading about in the press soon.

Seattle Washington, March 31, 2014.

Two neuroscientists at the University of Washington Medical School and a computer scientist from UW Tacoma have released results from a collaborative study of the brain that should cause quite a stir in brain science. Neuroscientists Drs. Mario Brothers and Gloria Swansong and computer scientist Dr. George Mobus have presented data that shows an anomalous situation in a brain area believed to be associated with higher judgment facilities. This anomaly — actually a seeming absence of critical neural tissue — was found in the vast majority of patients examined using functional magnetic resonance imaging (fMRI). The patients were being treated for other neurological problems not directly involving the region.

Drs. Brothers and Swansong were following up on a theory first proposed by Mobus that could actually explain a large number of puzzling problems with human behavior and thinking. Mobus' theory held that humans had undergone a prodigious evolutionary expansion in the region in question, the fronto-polar patch designated as Brodmann area 10 (BA10) — a small patch of specialized neural tissues right behind the eyebrows — and that the rapid expansion resulted in a loss of critical functions in this patch. He developed computer models that suggested that at the same time that humans had evolved super intelligence they lost their ability to make good decisions regarding long-term issues. The anomaly shows up as a small 'hole' in the center of BA10 where normally one expects to find densely packed neurons.

In 2012, Dr. Brothers had developed a method for measuring the efficacy of judgments made by people and had started mapping the brain regions involved in making such judgments. He started noticing a pattern of remarkably poor judgment in the majority of subjects, results he reported in Human Brain, a prestigious journal covering that subject. At the time, his colleague at the medical school, Dr. Swansong, had also been trying to localize brain regions involved in solving complex problems. She had noted that the fronto-polar region of the prefrontal cortex was explicitly not being engaged in subjects who were nonetheless able to solve complex problems. This was puzzling since it was assumed that higher-order judgments would play a part in intelligence. The two collaborated to develop an explanation for what their separate researches were suggesting. Their integrated methodology has been called the “stupidometer” because of the way it reveals how most humans make really poor decisions. They came across Mobus' theory and immediately began to suspect there might be something to it.

Now the scientists have gathered substantial data to show that the average human being is incapable of making higher-order judgments because of this apparent hole in their brains. Further evidence of humans choosing to solve problems that end up causing even greater negative consequences has been gathered.

“This could explain why we humans decided to develop industrial machines running on fossil fuels to exploit natural resources more rapidly even when the fact that such fuels were finite in scope and the burning of carbon produces the greenhouse gas carbon dioxide,” said Dr. Brothers. “I've often wondered how humans could believe that economic and population growth could go on for infinity when all of the science we know tells us not,” added Dr. Swansong. “Now I think we know,” she added, “Most people seem to have this ‘black hole’ in their brains where good judgments simply disappear through the event horizon.”

Not all people were found to have such holes. Out of two hundred patients tested, one proved to not have such a hole, or it may have been exceedingly small by comparison to the size of BA10. That patient's BA10 was found to be engaged while s/he was working on solving a difficult problem and s/he also scored extremely high on the judgment scale. Both neuroscientists later became curious about their own brains and subjected themselves to testing. Both were relieved to find that their holes were similarly very small or non-existing. No word on Dr. Mobus' condition.

The three researchers' paper has been submitted to Human Brain and is currently under peer review.

March 20, 2014

Spring is my absolutely favorite time of year. The leaves are starting to bud on most of the understory bushes, the cherry blossoms are bursting out, the days are generally warmer... What isn't to love about the world coming back to life? (With apologies to my Southern Hemisphere friends who are seeing the first signs of life going into dormancy!)

I'll be spending my Spring break putting some finishing touches on the systems science book. We've gotten a fair number of review comments to go by and have a June 1 target date for delivering the manuscript to the publisher! That has been quite a journey.

Last week I put in some lettuce plants. This weekend is time to plant cabbages, cauliflowers, and broccolis. Carrots too. Then, as time permits on to onions, garlics, potatoes, and maybe one or two other early planting crops. I don't garden so much for food, these days, as for the pure enjoyment of watching things grow. That I can enjoy their flavors when they mature is just a bonus.

I hope everyone (in the northern latitudes) will enjoy a relatively "normal" spring as weather goes. OK, I hope many of you do. There are already some anomalous weather patterns developing over the northern countries. Please stay safe.